Praneeth Ravirala

Data Engineer @ MetLife

About

I am a Data Engineer with 3+ years of experience building scalable distributed data pipelines and implementing modern lakehouse architectures across AWS, Azure, and GCP environments. At MetLife, I led the modernization of legacy Azure data platforms into Microsoft Fabric lakehouse architecture, architecting ingestion pipelines processing 2M+ records daily. I have built real-time streaming pipelines using Kafka, implemented Medallion architecture with Delta Lake, and optimized Spark transformations to improve data performance, reliability, and governance. Previously at Vivma Software, I delivered SaaS-based ERP data engineering solutions for enterprise audit clients, designed dimensional data models, and led on-prem to AWS cloud migrations using S3, Glue, Lambda, and Redshift. I have hands-on experience integrating Databricks for large-scale transformations and enabling reporting through Snowflake and BigQuery. My core strengths include: • Lakehouse & Medallion architecture • Real-time streaming pipelines (Kafka) • Spark-based distributed transformations • Multi-cloud data platforms (AWS, Azure, GCP) • CI/CD for data pipelines & DevOps integration • Data governance, RBAC & performance optimization I am passionate about designing reliable, scalable data systems that power enterprise analytics and business decision-making. Open to connecting with data engineering and cloud professionals.

Country

United States

City

Fairfax

Industry

Computer Software

Skill

Data Engineering, Data Architecture, Long Short-term Memory (LSTM), Generative Adversarial Networks (GANs), Multivariate Analysis, Feature Extraction, Feature Selection, Classification, Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, Performance Analysis, Database Management System (DBMS), Large Language Models (LLM), Data Cleaning, Data Transformation, Exploratory Data Analysis, Data Visualization, Geocoding

Experience

Data Engineer

MetLife

2024-8 - Present · 2 yrs 2 mos

• Led modernization of the legacy Azure data platform by migrating pipelines into a unified Microsoft Fabric lakehouse architecture and architected scalable ingestion pipelines processing 2M+ records daily from policy, claims, billing, and relational databases, improving data availability by 35% and reducing infrastructure overhead by 25%. • Enabled real time streaming pipelines using Kafka to ingest high volume event data, reducing batch processing dependency by 40%, lowering data egress costs by 20%, and improving data freshness from daily to near real time. • Implemented a Medallion architecture leveraging Delta Lake capabilities within Fabric and utilized Spark for advanced data transformations across Bronze, Silver, and Gold layers, reducing processing time by 30%. • Established automated data quality controls including schema validation, schema enforcement, and fail condition handling mechanisms, reducing production failures by 30% and decreasing downstream reporting errors by 25%. • Designed dimensional data models and loaded curated datasets into enterprise warehouse layers, improving query performance by 35% and accelerating reporting turnaround time by 20% through Power BI dashboards. • Implemented CI/CD practices using Azure DevOps and Git integration for data pipeline deployment and automated testing, reducing release cycles by 30% and improving deployment stability. • Led production monitoring and coordinated with support teams to proactively resolve pipeline. issues, maintaining 99.9% SLA compliance and reducing incident resolution time by 40%.

Data Engineer

Vivma Software Inc

2021-9 - 2023-7 · 1 yr 11 mos

• Delivered SaaS based ERP data engineering solutions for 5+ enterprise audit clients, designing dimensional models using star and snowflake schemas across Payroll, Supply Chain, HR, and Finance modules, improving reporting consistency by 30%. • Built transformation frameworks to standardize diverse client specific accounting structures into a unified dimensional data model, reducing manual reconciliation efforts by 35% and enhancing audit traceability. • Engineered migration of ERP datasets from on premise systems to AWS cloud infrastructure using Amazon S3, AWS Glue, Lambda, and Redshift, improving data accessibility by 40% and reducing legacy infrastructure dependency by 25%. • Integrated AWS pipelines with Databricks to leverage Spark for large scale data transformations, optimizing processing efficiency by 30% and improving batch performance for audit reporting workflows. • Enabled scalable analytical reporting by supporting data querying through Snowflake and Google BigQuery, improving cross platform data availability and reducing reporting turnaround time by 20%. • Served as primary engineering support contact for client production systems, troubleshooting schema changes and resolving pipeline failures, reducing incident resolution time by 40% while ensuring stable data delivery.